CALL FOR PAPER

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International Workshop on AI in Health: Transferring and Integrating Knowledge for Better Health

Co-located with The Web Conference 2021 (https://www2021.thewebconf.org)
https://suitclub.ischool.utexas.edu/AIHealthWWW2021/index.html
April 19, 2021, Ljubljana, Slovenia
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The rich medical concepts connected by semantic relationships integrate EHR data into knowledge graphs to enable knowledge-intensive discoveries. But it is still an open field with lots of challenges. For example, data cannot be easily shared across different hospital systems due to privacy, security, and policy issues. Especially, EHRs are embedded in different commercial vendor systems which makes the integration of EHRs extremely troublesome. But the recent development of FHIR and FAIR standards tackled this problem from a different angle that data can be communicated, integrated and analyzed simultaneously not only for physicians, but also available at the patient side. Biomedical ontologies and semantic web technologies can empower knowledge-driven discovery in healthcare to enable better cohort identification for clinical trials, risk prediction, precision diagnosis, and efficient clinical decision support workflows. Even though the dramatic increase of healthcare data offers unprecedented opportunities for evidence-based care, the interoperability of EHRs and mining the integrated EHRs are still open to innovative solutions. 

Building automatic or semi-automatic approaches on medical imaging diagnosis becomes the unavoidable next step. The combination of deep learning and prior knowledge of physicians organized as knowledge graphs can provide a powerful and yet unified framework for clinical decision support. It will open a new door to the potential of auto-annotating medical images by using AI and knowledge graph powered approaches. It can abruptly increase the annotated medical images at a much lower cost so that better CNN models can be trained, therefore better diagnosis models can be obtained. It can increase the interpretability of AI solutions by locating the abnormalities as the visual evidence in medical images which can build the trust between doctors and patients. 

In this workshop, we will welcome researchers from various domains to discuss and share latest progresses related to knowledge representation, computer vision, deep learning, knowledge graph, deep graph mining, and natural language processing to share latest developments to promote innovative semantic approaches to address pressing needs in healthcare.

The topics of this workshop include (but not limit to):
?	Knowledge representation and reasoning on healthcare data
?	Data integration, ontology and standards for healthcare data
?	Knowledge graph construction on healthcare data
?	Deep graph mining to address precision care
?	Biomedical ontology and Semantic Web technological applications in healthcare
?	Computer vision in medical imaging diagnosis
?	Auto-annotation of medical images
?	NLP for medical diagnosis notes
?	Multimodal deep learning models for advanced diagnosis
?	Interpretability of AI in health
?	Fairness of AI in health
?	AI applications in healthcare


Important Dates
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?	Workshop paper submissions: Jan 11, 2021
?	Workshop paper notifications: Feb 8, 2021
?	Camera-ready, March 1, 2021
?	Workshop date: April 19, 2021


Submission Guidelines
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Authors can submit either full papers of 8 pages in length or short papers of 4 pages length in the ACM format (https://www.acm.org/publications/proceedings-template), with the "sigconf" option. Since we plan to follow single-blind review process, there is no need to anonymize the author list. Submissions can be made using EasyChair (please check the website of the workshop for the link of easychair). 

High quality submissions with substantial revisions will be invited to submit to Data Intelligence Journal published by MIT Press (https://www.mitpressjournals.org/loi/dint).


Workshop Organizers
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Ying Ding, University of Texas at Austin, USA
Benjamin Glicksberg, Icahn School of Medicine at Mount Sinai, USA
Jim Hendler, RPI, USA
Mark Musen, Stanford, USA
Yifan Peng, Cornell University, USA
Fei Wang, Cornell University, USA
GQ Zhang, UT Health, USA
Marinka Zitnik, Harvard University and Broad Institute of Harvard and MIT, USA

Contact Information
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Ying Ding: ying.ding@austin.utexas.edu